Monitoring spatial and temporal variation of dissolved oxygen and water temperature in the Savannah River using a sensor network

Comparative analysis of sediment quality indices using different reference values in an environmental protection area in Southeastern Brazil

Contamination of aquatic ecosystems by potentially toxic elements (PTEs) is a concerning environmental issue, given their persistence, toxicity potential, and ability to accumulate in living organisms. Several studies have been conducted to assess the contamination of aquatic ecosystems by PTEs, using pollution and ecological risk indices that rely on the concentration of these elements in aquatic sediments. However, many of these studies use global reference values for calculating the indices, which can lead to misleading interpretations due to substantial variations in PTEs concentrations influenced by the geological characteristics of each region. Therefore, the use of regional reference values is more appropriate when available. This study aimed to investigate variations in the results of five indices, employing global, regional, and quality reference values, based on sediment samples collected from rivers in the Ipanema National Forest, a protected area in Brazil exposed to various anthropogenic pressures. The results revealed that elements such as Al, Fe, and Mn exceeded the limits allowed by legislation in water samples, while As and Cr surpassed the limits in sediment samples. Comparative analysis highlighted significant discrepancies in the results of the indices when global reference values were used compared to regional and quality reference values, especially for As and Ba. Thus, this study underscores the importance of establishing specific regional values for an accurate assessment of sediment quality and the risks associated with contamination by PTEs in different regions worldwide.

Research progress in water quality prediction based on deep learning technology: a review

Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices, enabling evidence-based decision-making and targeted environmental interventions. The emergence of advanced computational technologies, particularly deep learning, has garnered considerable interest among researchers for applications in water quality prediction because of its robust data analytics capabilities. This article comprehensively reviews the deployment of deep learning methodologies in water quality forecasting, encompassing single-model and mixed-model approaches. Additionally, we delineate optimization strategies, data fusion techniques, and other factors influencing the efficacy of deep learning-based water quality prediction models, because understanding and mastering these factors are crucial for accurate water quality prediction. Although challenges such as data scarcity, long-term prediction accuracy, and limited deployments of large-scale models persist, future research aims to address these limitations by refining prediction algorithms, leveraging high-dimensional datasets, evaluating model performance, and broadening large-scale model application. These efforts contribute to precise water resource management and environmental conservation.

Sampling frequency optimization of the water quality monitoring network in São Paulo State (Brazil) towards adaptive monitoring in a developing country

Water quality monitoring networks (WQMNs) that capture both the temporal and spatial dimensions are essential to provide reliable data for assessing water quality trends in surface waters, as well as for supporting initiatives to control anthropogenic activities. Meeting these monitoring goals as efficiently as possible is crucial, especially in developing countries where the financial resources are limited and the water quality degradation is accelerating. Here, we asked if sampling frequency could be reduced while maintaining the same degree of information as with bimonthly sampling in the São Paulo State (Brazil) WQMN. For this purpose, we considered data from 2004 to 2018 for 56 monitoring sites distributed into four out of 22 of the state's water resources management units (UGRHIs, "Unidades de Gerenciamento de Recursos Hídricos"). We ran statistical tests for identifying data redundancy among two-month periods in the dry and wet seasons, followed by objective criteria to develop a sampling frequency recommendation. Our results showed that the reduction would be feasible in three UGRHIs, with the number of annual samplings ranging from two to four (instead of the original six). In both seasons, dissolved oxygen and Escherichia coli required more frequent sampling than the other analyzed parameters to adequately capture variability. The recommendation was compatible with flexible monitoring strategies observed in well-structured WQMNs worldwide, since the suggested sampling frequencies were not the same for all UGRHIs. Our approach can contribute to establishing a methodology to reevaluate WQMNs, potentially resulting in less costly and more adaptive strategies in São Paulo State and other developing areas with similar challenges.

Influence of water-level variability on fish assemblage and natural reproduction following connectivity enhancement in a Typha-dominated coastal wetland, USA

We evaluated a wetland habitat modification strategy to contrast fish assemblage structure and the production of young-of-the-year (YOY) fish between different engineered habitats (i.e., spawning pool complexes and connectivity channels) relative to unmodified lateral channels in a large drowned river mouth tributary of the St Lawrence River. Prior to habitat modifications, the coastal wetland was impaired by water level regulations and dominance of the invasive hybrid cattail, Typha × glauca, which collectively replaced or created barriers to seasonally flooded spawning habitats important to fish. Connectivity enhancements provided fish access along a wetland habitat gradient from sedge-meadows to the deeper water robust emergent main channel. Across an 8-year fish emigration dataset (2012, 2013, 2016-2021) more than 90% of all captured fish (Ntotal  = 218,086 fish) were YOY and modified habitats outperformed the unmodified channels in total fish catch-per-unit-effort (CPUE) per year (both YOY and non-YOY). Spawning pool complexes had higher YOY species richness than unmodified channel habitats. Fish assemblage structure differed between the modified habitats, where connectivity channels and unmodified channels shared a more similar fish assemblage than spawning pool complexes. Modified habitats, however, supported warmer water and higher dissolved oxygen than the unmodified channels. Redundancy analysis and linear mixed-effect modelling with abiotic variables (hydrology, temperature and dissolved oxygen) showed significant effects on fish assemblage structure, species richness and CPUE of fish emigrating from the modified and unmodified habitats. Historic flooding in 2017 and 2019 was a primary driver of YOY fish production and fish assemblage structure, but also appeared to be associated with near anoxic conditions systemwide. YOY fish for several species was inversely affected by floods at spawning pool complexes, but CPUE of YOY fish for these species appeared unaffected at the connectivity channels despite low dissolved oxygen. Diversified habitat structure (i.e., connectivity channels and spawning pool complexes) offers a management option to enhance habitat for fish that allowed compensatory effects on the capture of YOY fish of several species during floods. This multifaceted outcome from the habitat modifications resulted in unique fish assemblages between the channelized and spawning pool habitat. A connectivity-based habitat enhancement strategy provides adaptability for an uncertain climatic and regulatory future for the Laurentian Great Lakes and St Lawrence River.

Brevitentoria Weaver 1984 (Trichoptera: Integripalpia) of Esprito Santo State, Brazil: New records and new species

In Brazil, about 900 species of Trichoptera have been recorded, with some species in all Brazilian states. Nevertheless, the collection effort is unequal, with several under-sampled regions. Despite being located entirely within the Atlantic Forest ecoregion, a hotspot of biodiversity, Esprito Santo State has a low known caddisfly richness when compared to nearby areas in the same ecoregion, especially for the infraorder Brevitentoria. This suggests the existence of a Trichoptera biodiversity knowledge gap. Aiming to overcome these taxonomic and distributional shortfalls, we performed a comprehensive inventory of the Brevitentoria species in the state. The sampled sites were distributed from North to South of the state covering 22 locations. In total, 3,420 adult specimens of Brevitentoria were analyzed, leading to a total of 40 species. Two families and 27 species are recorded for the first time from the state. Additionally, we describe three new species of the genera Phylloicus, Helicopsyche (Feropsyche), and Marilia. As a result of this survey, we increase by 100% the number of species of Brevitentoria known from the state, and by 30% for the number of known Trichoptera species. Based on incidence data from this inventory and from the literature, the Brevitentoria species richness was estimated to be about 72 species in Esprito Santo State.

Comparison of machine learning algorithms to predict dissolved oxygen in an urban stream

Water quality monitoring for urban watersheds is critical to identify the negative urbanization impacts. This study sought to identify a successful predictive machine learning model with minimal parameters from easy-to-deploy, low-cost sensors to create a monitoring system for the urban stream network, Hunnicutt Creek, in Clemson, SC, USA. A multiple linear regression model was compared to machine learning algorithms k-nearest neighbor, decision tree, random forest, and gradient boosting. These algorithms were evaluated to understand which best predicted dissolved oxygen (DO) from water temperature, conductivity, turbidity, and water level change at four locations along the urban stream. The random forest algorithm had the highest performance in predicting DO for all four sites, with Nash-Sutcliffe model efficiency coefficient (NSE) scores > 0.9 at three sites and > 0.598 at the fourth site. The random forest model was further examined using explainable artificial intelligence (XAI) and found that temperature influenced the DO predictions for three of the four sites, but there were different water quality interactions depending on site location. Calculating the land cover type in each site's sub-watershed revealed that different amounts of impervious surface and vegetation influenced water quality and the resulting DO predictions. Overall, machine learning combined with land cover data helps decision-makers better understand the nuances of urban watersheds and the relationships between urban land cover and water quality.

The effect of piping stream channels on dissolved oxygen concentration and ecological health

Sunlight plays a key role in the nutrient cycle within streams. Streams are often piped to accommodate urban residential or commercial development for buildings, roads, and parking. This results in altered exposure to sunlight, air, and soil, subsequently affecting the growth of aquatic vegetation, reducing reaeration, and thus impairing the water quality and ecological health of streams. While the effects of urbanization on urban streams, including changing flow regimes, stream bank and bed erosion, and degraded water quality, are well understood, the effects of piping streams on dissolved oxygen (DO) concentrations, fish habitats, reaeration, photosynthesis, and respiration rates are not. We addressed this research gap by assessing the effects of stream piping on DO concentrations before and after a 565-m piped section of Stroubles Creek in Blacksburg, VA, for several days during the summer of 2021. Results indicate that the DO level decreased by approximately 18.5% during daylight hours as water flowed through the piped section of the creek. Given the optimum DO level (9.0 mg·L^-1) for brook trout (Salvelinus sp.), which are native and present in a portion of Stroubles Creek, the resulting DO deficits were - 0.49 and - 1.24 mg·L^-1, for the inlet and outlet, respectively, indicating a possible adverse impact from piping the stream on trout habitat. Photosynthesis and respiration rates were reduced through the piped section, primarily due to the reduced solar radiation and the resultant reduction in oxygen production from aquatic vegetation; however, the reaeration rate increased. This study can inform watershed restoration efforts, particularly decisions regarding stream daylighting with respect to potential water quality and aquatic habitat benefits.

Predicting Fecal Indicator Bacteria Using Spatial Stream Network Models in A Mixed-Land-Use Suburban Watershed in New Jersey, USA

Good water quality safeguards public health and provides economic benefits through recreational opportunities for people in urban and suburban environments. However, expanding impervious areas and poorly managed sanitary infrastructures result in elevated concentrations of fecal indicator bacteria and waterborne pathogens in adjacent waterways and increased waterborne illness risk. Watershed characteristics, such as urban land, are often associated with impaired microbial water quality. Within the proximity of the New York-New Jersey-Pennsylvania metropolitan area, the Musconetcong River has been listed in the Clean Water Act's 303 (d) List of Water Quality-Limited Waters due to high concentrations of fecal indicator bacteria (FIB). In this study, we aimed to apply spatial stream network (SSN) models to associate key land use variables with E. coli as an FIB in the suburban mixed-land-use Musconetcong River watershed in the northwestern New Jersey. The SSN models explicitly account for spatial autocorrelation in stream networks and have been widely utilized to identify watershed attributes linked to deteriorated water quality indicators. Surface water samples were collected from the five mainstem and six tributary sites along the middle section of the Musconetcong River from May to October 2018. The log₁₀ geometric means of E. coli concentrations for all sampling dates and during storm events were derived as response variables for the SSN modeling, respectively. A nonspatial model based on an ordinary least square regression and two spatial models based on Euclidean and stream distance were constructed to incorporate four upstream watershed attributes as explanatory variables, including urban, pasture, forest, and wetland. The results indicate that upstream urban land was positively and significantly associated with the log₁₀ geometric mean concentrations of E. coli for all sampling cases and during storm events, respectively (p < 0.05). Prediction of E. coli concentrations by SSN models identified potential hot spots prone to water quality deterioration. The results emphasize that anthropogenic sources were the main threats to microbial water quality in the suburban Musconetcong River watershed. The SSN modeling approaches from this study can serve as a novel microbial water quality modeling framework for other watersheds to identify key land use stressors to guide future urban and suburban water quality restoration directions in the USA and beyond.

Spatially distributed water quality responses to freshwater discharge in a tropical estuary, Hilo Bay, Hawai'i

Hilo Bay, Hawai'i, is an estuary of great importance to its neighboring coastal community, but it is threatened by impaired water quality indicated by excessive turbidity and chlorophyll a associated with river discharges of sediments and nutrients. The Wailuku River in the western half of the bay is the primary source of freshwater discharge, hypothesized here to form a surface water-dominant half of the bay with different water quality traits than the groundwater-dominant, eastern half of the bay where the spring-fed Wailoa River discharges. The water quality of both halves of the bay over different flow conditions of the Wailuku River is examined in this study using spatially distributed water quality sampling which collects hundreds of samples in either half of the bay at a distance of about every 40 m. The dense sample shows significant differences between the two halves of the bay, with greater salinity dilution and turbidity in the surface water-dominant area. Both salinity and turbidity have a predictable relation to discharge, with salinity decreasing and turbidity increasing in higher flow conditions. Chlorophyll a, however, has a more complex relation to discharge, as chlorophyll a concentrations are greatest in high-flow conditions, but this may be because the water quality samples were collected in different seasons. Furthermore, significantly greater chlorophyll a concentrations in the groundwater-dominant half of the bay in low-flow conditions show that discharge may be spuriously correlated to chlorophyll a, and further studies of the effects of surface water discharge on chlorophyll a concentrations are warranted.

Combined Effect of Climate and Anthropopressure on River Water Quality

This study was a continuation of our investigation of the spatio-temporal variability of the Bzura River's water chemistry. Our research is of particular importance in the context of the recent ecological disaster on the Oder River and concerns the international problem of surface water contamination. The study area was a 120 km section of the Bzura River. We tested more measurement points and with a higher sampling frequency than those used in the national monitoring of river water quality. During two hydrological years, 360 water samples were collected. The selected parameters: electrical conductivity, temperature, dissolved oxygen, dissolved organic carbon, nitrates, phosphates, bicarbonates, chlorides, sodium, potassium, calcium, and magnesium were determined. Numerous results exceeded the Polish threshold limits. Spatio-temporal variability and water quality were assessed using principal component analysis (PCA), cluster analysis (CA), and water quality index (WQI) approaches. Many point sources of pollution related to urbanization, agriculture, and industry were detected. Moreover, due to the changing climatic conditions, a significant difference between temporal variability in both years was observed. Our results indicated that it is necessary to increase the number of measurement stations for surface water monitoring; it will allow for a faster detection of the threat.

The potential of novel hybrid SBO-based long short-term memory network for prediction of dissolved oxygen concentration in successive points of the Savannah River, USA

The accurate estimation of dissolved oxygen (DO) as an important water quality indicator can provide a basis for ensuring the preservation of the riverine ecosystem and designing proper water quality development plans. Therefore, this study aimed to propose a novel hybrid model based on long short-term memory (LSTM) networks with Satin Bowerbird optimizer (SBO) algorithm for the estimation of the DO concentration based on multiple water quality parameters. Furthermore, to compare the supreme performance of proposed hybrid model, standalone LSTM, support vector machine (SVM) and Gaussian process regression (GPR) were employed. The models were prepared using the datasets collected from three successive gauging stations along the Savannah River, USA, for the period 2015-2021. The modeling process was performed through local and cross-station scenarios to assess the interrelations between the DO values of upstream/downstream stations. The comparison of estimation accuracies of different employed models revealed that the proposed SBO-LSTM yields a correlation coefficient (R) of 0.981, Nash-Sutcliffe efficiency (NSE) of 0.957, and root mean square error (RMSE) of 0.034 for a test series of dissolved oxygen series which was the most accurate model through both local and cross-station scenarios. Also, the proposed SBO-LSTM model showed better performance by 0.52% and 1.26% than employed SVM and GPR models, respectively. The obtained results showed the essential role of the water temperature parameter in the DO modeling of all three studied stations.

The biogeography of colonial volvocine algae in the Yangtze River basin

Colonial Volvocine Algae (CVA) are of great significance for biological evolution study, but little is presently known about their biogeographic distribution. Meanwhile, with the impact of climate change and human activities, their effects on the distribution and structures of CVA communities also remain largely unknown. Herein, the biogeography of CVA was investigated in the Yangtze River basin, 172 sampling sites were set up within a catchment area of 1,800,000  km², and the distribution and community composition of CVA were studied using single-molecule real-time sequencing and metabarcoding technology based on the full-length 18S sequence. In 76 sampling sites, CVA was discovered in two families, eight genera, and nine species. Eudorina and Colemanosphaera were the main dominant genus. Based on the result of the random forest model and Eta-squared value, the distribution of CVA was significantly influenced by water temperature, altitude, and TP. CVA could be suitably distributed at an average water temperature of 22°C, an average TP concentration of 0.06 mg/L, and an altitude lower than 3,920  m. To assess the effects of anthropogenic pollution on the structures and co-occurrence patterns of CVA communities, we used a stress index calculated by 10 environmental factors to divide the CVA community into low and high pollution group. Network analysis showed that greater pollution levels would have a negative impact on the co-occurrence patterns and diversity of the CVA community. Finally, to study the scientific distribution of CVA under current and future climate change scenarios, we analyzed the climate suitability regionalization of CVA with the maximum entropy model based on 19 climatic factors and four climate scenarios from 2021 to 2040 published by CMIP6. Our results reveal the suitable areas of CVA, and temperature is an important environmental factor affecting the distribution of CVA. With the change of climate in the future, the Three Gorges Reservoir Area, Chaohu Lake, and Taihu Lake are still highly suitable areas for CVA, but the habitat of CVA may be fragmented, and more thorough temporal surveys and sampling of the sediment or mud are needed to investigate the fragmentation of CVA.

Evaluation of the water quality of a highly polluted stream with water quality indices and health risk assessment methods

The water quality of Çorlu Stream, located in the Thrace region of Türkiye, and exposed to intense industrial pressure, was evaluated by monitoring 10 toxic metals and 13 other water quality variables in the dry and wet seasons of 2021. Seven different water quality indices were applied to determine the pollution level at the sampling stations in the stream. In addition, human health risks from exposure to toxic metals in stream water via ingestion and dermal contact were evaluated. The results showed that the water quality at stations S2 and S3 of Çorlu Stream receiving domestic and industrial discharges are seriously polluted by NH4-N, PO4-P, COD, BOD5 and suspended solids according to surface water quality standards. In addition, these stations were highly polluted and had poor water quality according to the results of the water quality indices. The average Cr level at station S3 exceeded the permissible levels set for the protection of aquatic life due to effluent discharges from the leather factories. Considering the results of the health risk assessment methods, non-carcinogenic risks from ingestion of combined metals in stream water can be expected at station S3 for both children and adults and at station S2 for children. Also, it was estimated that Cr and As at station S3 may cause carcinogenic health risks for residents.

Evaluating the eutrophication risk of artificial lagoons-case study El Gouna, Egypt

Eutrophication problem in El Gouna shallow artificial coastal lagoons in Egypt was investigated using 2D TELEMAC-EUTRO-WAQTEL module. Eight reactive components were presented, among them dissolved oxygen (DO), phosphorus, nitrogen, and phytoplankton biomass (PHY). The effect of warmer surface water on the eutrophication problem was investigated. Also, the spatial and temporal variability of the eutrophication was analyzed considering different weather conditions: tide wave, different wind speeds and directions. Moreover, effect of pollution from a nearby desalination plant was discussed considering different pollution degrees of brine discharge, different discharge quantities and different weather conditions. Finally, new precautions for better water quality were discussed. The results show that tide wave created fluctuations in DO concentrations, while other water quality components were not highly influenced by tide's fluctuations. Also, it was found that high water temperatures and low wind speeds highly decreased water quality producing low DO concentrations and high nutrients rates. High water quality was produced beside inflow boundaries when compared to outflow boundaries in case of mean wind. Moreover, the results show that the average water quality was not highly deteriorated by the nearby desalination operation, while the area just beside the desalination inflow showed relatively strong effects. Different weather conditions controlled the brine's propagation inside the lagoons. Moreover, increasing the width of the inflow boundaries and injecting tracer during tide and mean wind condition are new precautions which may help to preserve the water quality in a future warmer world. This study is one of the first simulations for eutrophication in manmade lagoons.

Evaluation of a new low-cost particle sensor as an internet-of-things device for outdoor air quality monitoring

Many low-cost particle sensors are available for routine air quality monitoring of PM_2.5, but there are concerns about the accuracy and precision of the reported data, particularly in humid conditions. The objectives of this study are to evaluate the Sensirion SPS30 particulate matter (PM) sensor against regulatory methods for measurement of real-time particulate matter concentrations and to evaluate the effectiveness of the Intelligent Air^TM sensor pack for remote deployment and monitoring. To achieve this, we co-located the Intelligent Air^TM sensor pack, developed at Clemson University and built around the Sensirion SPS30, to collect data from July 29, 2019, to December 12, 2019, at a regulatory site in Columbia, South Carolina. When compared to the Federal Equivalent Methods, the SPS30 showed an average bias adjusted R² = 0.75, mean bias error of -1.59, and a root mean square error of 2.10 for 24-hour average trimmed measurements over 93 days, and R² = 0.57, mean bias error of -1.61, and a root mean square error of 3.029, for 1-hr average trimmed measurements over 2300 hours when the central 99% of data was retained with a data completeness of 75% or greater. The Intelligent Air^TM sensor pack is designed to promote long-term deployment and includes a solar panel and battery backup, protection from the elements, and the ability to upload data via a cellular network. Overall, we conclude that the SPS30 PM sensor and the Intelligent Air^TM sensor pack have the potential for greatly increasing the spatial density of particulate matter measurements, but more work is needed to understand and calibrate sensor measurements.Implications: This work adds to the growing body of research that indicates that low-cost sensors of particulate matter (PM) for air quality monitoring has a promising future, and yet much work is left to be done. This work shows that the level of data processing and filtering effects how the low-cost sensors compare to existing federal reference and equivalence methods: more data filtering at low PM levels worsens the data comparison, while longer time averaging improves the measurement comparisons. Improvements must be made to how we handle, calibrate, and correct PM data from low-cost sensors before the data can be reliably used for air quality monitoring and attainment.

Assessment of dissolved mercury by diffusive gradients in thin films devices in abandoned ponds impacted by small scale gold mining

In order to fulfil the Minamata Convention on Mercury, it is necessary to monitor the Hg contamination in freshwater ecosystems nearby artisanal and small scale gold mining (ASGM) areas. Since most of these ASGM communities are located in remote areas, a convenient method for sampling, preserving and transporting samples is needed. In this study we evaluated the feasibility of the diffusive gradient in thin-films (DGT) technique to detect and quantify the labile fraction of Hg and other metals (Pb, Cu, Zn, Cd, Ni, Mn and Cr) in a hard-to-reach gold mining district in the state of Chocó, Colombia. We deployed DGT at sampling sites along the Atrato river and abandoned mining ponds (AMPs) which were deserted in different periods since 1997 to 2019 (6-15 years). In average, the labile THg concentrations in AMPs (148.9 ± 43.2 ng L^-1) were a 50% higher than in the river water (99.9 ± 37.4 ng L^-1). In the ponds, no significant differences were found in labile Hg with respect abandonment period. Labile Ni (0.9-493.1), Mn (1.33-11.48), Cu (0.030-2.233), and Zn (0.67-10.29) (in μg L^-1) were found in higher amounts than for the rest of metals. Labile concentrations of metals are related with their downstream proximity to gold mining activities, being higher in devices deployed close to ASGM sites. Moreover, this study demonstrates the feasibility of the DGT technique to sample, transport, storage, and preserve labile Hg from hard-to-reach ASGM areas.

Impact of the COVID-19 lockdown period on surface water quality in the Meriç-Ergene River Basin, Northwest Turkey

The surface water resources in the Meriç-Ergene River Basin, especially Ergene River and Çorlu Stream are among the most polluted rivers in Turkey. Despite the action plans for prevention and control of surface water pollution in the basin, the desired results have not been achieved. However, the implementation of a nationwide lockdown due to the COVID-19 might probably lead to an improvement in the surface water quality. We evaluated the impact of the lockdown on water quality by measuring the levels of physico-chemical variables and metal(loid)s in water samples taken from 25 sampling stations in the basin. BOD, COD, EC, turbidity, TSS and Mn levels did not show significant differences between the pre-lockdown and lockdown periods due to the ongoing domestic wastewater discharges and agricultural activities in the basin during the lockdown period. However, Cr, Ni, Zn, Cu, As, Pb and Cd concentrations decreased considerably during the lockdown. Similarly, heavy metal pollution index and heavy metal evaluation index values showed a significant improvement in water quality of almost all stations during the lockdown period. Also, total hazard index values for children and adults reduced by 67% and 69%, respectively during the lockdown period, while total carcinogenic risk values for As and Cr reduced by 60% and 94%, respectively. The limited operational status of most industrial facilities in the basin during the lockdown reduced the amount of industrial effluents, leading to significant improvement in surface water quality for metal(loid)s. The lockdown has shown that the solution for preservation and sustainability of natural water resources lies in our hands, and the efficient management of pollution sources can prevent surface water pollution at a very rapid pace. Finally, we suggest that water management policy needs to be improved and implemented.

Use of water quality index and multivariate statistical methods for the evaluation of water quality of a stream affected by multiple stressors: A case study

The Sürgü Stream, located in the Euphrates River basin of Turkey, is used for drinking water source, agricultural irrigation and rainbow trout production. Therefore, water quality of the stream is of great importance. In this study, multivariate statistical techniques (MSTs) and water quality index (WQI) were applied to assess water quality of the stream affected by multiple stressors such as untreated domestic sewage, effluents from fish farms, agricultural runoff and streambank erosion. For this, 16 water quality parameters at five sites along the stream were monitored monthly during one year. Most of parameters showed significant spatial variations, indicating the influence of anthropogenic activities. All parameters except TN (total nitrogen) showed significant seasonal differences due to high seasonality in WT (water temperature) and water flow. The spatial variations in the WQI were significant (p < 0.05) and the mean WQI values ranged from 87.6 to 95.3, indicating "good" to "excellent" water quality in the stream. Cluster analysis classified five sites into three groups, that is, clean region, low polluted region and very clean region. Stepwise temporal discriminant analysis (DA) identified that pH, WT, Cl^-, SO₄^2-, COD (chemical oxygen demand), TSS (total suspended solids) and Ca²⁺ are the parameters responsible for variations between seasons, and stepwise spatial DA identified that DO (dissolved oxygen), EC (electrical conductivity), NH₄-N, TN (total nitrogen) and TSS are the parameters responsible for variations between the regions. Principal component analysis/factor analysis revealed that the parameters responsible for water quality variations were mainly associated with suspended solids (both natural and anthropogenic), soluble salts (natural) and nutrients and organic matter (anthropogenic).

Preparing GIS data for analysis of stream monitoring data: The R package openSTARS

Stream monitoring data provides insights into the biological, chemical and physical status of running waters. Additionally, it can be used to identify drivers of chemical or ecological water quality, to inform related management actions, and to forecast future conditions under land use and global change scenarios. Measurements from sites along the same stream may not be statistically independent, and the R package SSN provides a way to describe spatial autocorrelation when modelling relationships between measured variables and potential drivers. However, SSN requires the user to provide the stream network and sampling locations in a certain format. Likewise, other applications require catchment delineation and intersection of different spatial data. We developed the R package openSTARS that provides the functionality to derive stream networks from a digital elevation model, delineate stream catchments and intersect them with land use or other GIS data as potential predictors. Additionally, locations for model predictions can be generated automatically along the stream network. We present an example workflow of all data preparation steps. In a case study using data from water monitoring sites in Southern Germany, the resulting stream network and derived site characteristics matched those constructed using STARS, an ArcGIS custom toolbox. An advantage of openSTARS is that it relies on free and open-source GRASS GIS and R functions, unlike the original STARS toolbox which depends on proprietary ArcGIS. openSTARS also comes without a graphical user interface, to enhance reproducibility and reusability of the workflow, thereby harmonizing and simplifying the data pre-processing prior to statistical modelling. Overall, openSTARS facilitates the use of spatial regression and other applications on stream networks and contributes to reproducible science with applications in hydrology, environmental sciences and ecology.

Tolerance traits related to climate change resilience are independent and polygenic

The resilience of organisms to climate change through adaptive evolution is dependent on the extent of genetically based variation in key phenotypic traits and the nature of genetic associations between them. For aquatic animals, upper thermal tolerance and hypoxia tolerance are likely to be a important determinants of sensitivity to climate change. To determine the genetic basis of these traits and to detect associations between them, we compared naturally occurring populations of two subspecies of Atlantic killifish, Fundulus heteroclitus, that differ in both thermal and hypoxia tolerance. Multilocus association mapping demonstrated that 47 and 35 single nucleotide polymorphisms (SNPs) explained 43.4% and 51.9% of variation in thermal and hypoxia tolerance, respectively, suggesting that genetic mechanisms underlie a substantial proportion of variation in each trait. However, no explanatory SNPs were shared between traits, and upper thermal tolerance varied approximately linearly with latitude, whereas hypoxia tolerance exhibited a steep phenotypic break across the contact zone between the subspecies. These results suggest that upper thermal tolerance and hypoxia tolerance are neither phenotypically correlated nor genetically associated, and thus that rates of adaptive change in these traits can be independently fine-tuned by natural selection. This modularity of important traits can underpin the evolvability of organisms to complex future environmental change.

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.